An ECG procedure measures cardiac electrical potentials that can be graphed to visually depict the electrical activity of the heart over time and allows physicians to diagnose cardiac function by visually tracing the cutaneous electrical signals (action potentials) that are generated by the propagation of the transmembrane ionic currents that trigger the depolarization of cardiac fibers. Conventionally, a standardized set format 12-lead configuration is used by an ECG machine to record cardiac electrical signals from well-established traditional chest locations.
An ECG trace contains alphabetically-labeled waveform deflections, PQRSTU, that represent distinct features within the cyclic cardiac activation sequence and that can be interpreted post-ECG recordation to derive heart rate and physiology and for use in medical diagnosis and treatment. The P-wave represents atrial depolarization, which causes atrial contraction. The QRS-complex represents ventricular depolarization and is the largest electrical signal of the heart. The T-wave and U-wave represents ventricular repolarization. The T and U-waves are not usually used in diagnosing most cardiac rhythm disorders and are included for completeness.
Here, the focus is around the R-wave, which is often used as an abbreviation for the QRS-complex. When measuring the time between an R-R interval, one can get a beat-by-beat assessment of heart rate. Typically, the R-R interval span between successive R-waves, in a normal heart, is 600 milliseconds (ms) to 1000 ms (i.e., one second) long, which respectively corresponds to 100 to 60 beats per minute (bpm). If one further considers the R-R interval as occurring over time, beat-by-beat, detailed cardiac physiology data may be embedded to provide information that allows a physician to understand, at a glance, the context of the associated ECG rhythm both before and after a suspected rhythm abnormality and can be of confirmational and collaborative value in cardiac arrhythmia diagnosis and treatment.
Conventionally, the potential of R-R interval context has not been fully realized, partly due to the difficulty of presentation in a concise and effective manner to physicians. For instance, routine ECGs are typically displayed at an effective paper speed of 25 millimeters (mm) per second. A lower speed is not recommended because ECG graph resolution degrades at lower speeds and diagnostically-relevant features may be lost. Conversely, a half-hour ECG recording, progressing at 25 mm/s, results in 45 meters of ECG waveforms that, in printed form, is cumbersome and, in electronic display form, will require significant back and forth toggling between pages of waveforms, as well as presenting voluminous data transfer and data storage concerns. As a result, ECGs are less than ideal tools for diagnosing cardiac arrhythmia patterns that only become apparent over an extended time frame, such as 10 minutes or longer.
In addition to or in lieu of physician review, the R-wave data can be provided to analysis software for identification of cardiac events and patient diagnosis. However, such software generally operates best on datasets that represent a few minutes to two days of ECG data. As newer ECG monitoring devices enter the market, many include longer data recording periods. Accordingly, such analysis programs are overloaded with data, which can hinder effectiveness of the analysis, leading to an incorrect patient diagnosis.
Further, performing an analysis of data that includes large amounts of noise can affect accuracy of patient diagnosis. For instance, noise can mask portions of the ECG data, which can prevent analysis software or a medical professional from identifying patterns of cardiac events, as well as lead to an incorrect diagnosis or lack of a diagnosis.
Therefore, a need remains for improving quality of ECG data, while reducing amounts of the ECG data for analysis, including patient diagnosis, by identifying and removing noise. Such noise can be recognized by differentiating regions of valid ECG data from noise using an adaptive noise detector and the regions of noise can be removed from the valid ECG data.